风力机叶片裂纹扩展预测与疲劳损伤评价

CRACK GROWTH PREDICTION AND FATIGUE DAMAGE EVALUATION ON WIND TURBINE BLADE

针对风力机叶片疲劳损伤过程难以定量评价的问题,提出一种基于裂纹扩展AE信号分形特征的疲劳损伤模糊评价方法。首先用修正系数μ改进关联维数的计算式,确定32组试样所适合的修正系数和最佳嵌入维数,然后在裂纹扩展试验中将AE信号的关联维数与加载条件、裂纹结构参数等共同组成评价疲劳损伤的影响因素集合,从试样数据中在集合近似的条件下求得权重集合,最后对某风场1.5 MW的风力机叶片进行评价,为探索风力机叶片疲劳损伤状态和裂纹扩展之间的内在规律提供了一条新思路。

from crack The fuzzy evaluation method of fatigue damage was presented based on fractal dimension feature extracted growth AE signals to resolve the difficult problem of fatigue damage quantitative evaluation for wind turbine blade. Firstly, the dimension expressions are improved by the coefficient μand the optimized embedded dimension adapted for 32 samples we ascertained. In the crack growth experiment, the correlation dimension becomes one of the affect factors set a combined with the crack location, load and configuration of the fatigue crack. The weightiness matrix was calculated conditioned by the approximate matrix by the data collected from all the samples. Finally, the fatigue condition of 1.5 MW wind turbine blade in a wind field was evaluated and a new method was explored to understand the association mechanism between the damage condition and crack growth for wind turbine blade.